CODEWITHSUNDEEP
matlabpcaeigenvectoreigenvalue
user3168226
user3168226

Reputation: 57

What does eigenvalues represent in Face Recognition by Eigenfaces

I've got a set of training face images (40 images). Each image size is 28*34. From there, I'm able to get eigenVector, Score, Latent using princomp function in Matlab.

I've got 952 latents (eigenvalues in covariance matrix) which are in descending form : 4.2785 to 0 . Eigenvalues are zeros from k=40 onwards.

May i know what does the the eigenvalues indicate ? (say bigger value means more significant to variance?) how could I identify the best k value (Principal component)?

Thank you so much for your help !

Upvotes: 0

Views: 725

Answers (2)

Jai
Jai

Reputation: 3300

  • EigenValue of an eigenvector represents how important you eigenvector is.
  • Higher the Eigenvalue more important the eigentvector is
  • for 40 images the eigenvectors can not be more than 40
  • You can reconstruct the faces with the eigencvectors to visualize how it looks like
  • The eigenvector with highest eigenvalue is a principal component where most of the data lies

Upvotes: 0

Shai
Shai

Reputation: 114866

Since you only have 40 input faces you cannot expect to have more than 40 principal components. Therefore the eigenvalue becomes zero for K=40 onwards.
To visualize your results, take the 40 leading eigen vectors, reshape them back to 28-by-34 and imagesc them. What have you got?

Upvotes: 1

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